C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\entityset\entityset.py:1914: UserWarning: index SK_BUREAU_ID not found in dataframe, creating new integer column warnings.warn( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\woodwork\type_sys\utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime( C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function min at 0x0000021834D54400> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead. ).agg(to_agg) C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function max at 0x0000021834D542C0> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead. ).agg(to_agg) C:\Users\Paulius\AppData\Local\pypoetry\Cache\virtualenvs\ppuodz-ml-4-1-dqELbViF-py3.12\Lib\site-packages\featuretools\computational_backends\feature_set_calculator.py:828: FutureWarning: The provided callable <function mean at 0x0000021834D54CC0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead. ).agg(to_agg)
Appending previous history Full DS size: 307511
Exploratory Analysis¶
This notebook includes the analysis of selected variables (based on their importance (based on SHAP values) at predicting the target variable) and their relationships. Individual analysis of each variable is available in the EDA_appendices notebook.
Dataset Summary¶
NaN Values by Column:
Out[6]:
| Total NaN Values | Proportion NaN (%) | |
|---|---|---|
| ExtSource2 | 660 | 0.0 |
| ExtSource3 | 60965 | 20.0 |
| AmtGoodsPrice | 278 | 0.0 |
| ExtSource1 | 173378 | 56.0 |
| OwnCarAge | 202929 | 66.0 |
| MeanbureauamtCreditSumDebt | 51380 | 17.0 |
| AmtAnnuity | 12 | 0.0 |
| PrevAvgYieldGroup | 18945 | 6.0 |
| PrevCreditReceivedRequestedDiff | 16454 | 5.0 |
| PrevAmtDownPaymentSum | 16454 | 5.0 |
| MaxbureaudaysCredit | 44020 | 14.0 |
| MaxbureaudaysCreditEnddate | 46269 | 15.0 |
| OccupationType | 96391 | 31.0 |
| MaxbureaudaysEnddateFact | 77156 | 25.0 |
| MeanbureauamtCreditSum | 44021 | 14.0 |
Duplicates¶
'Duplicated Values: 0'
'Total Columns: 229'
Correlations¶
Because we have such a large number of columns we have only included features which have an importance value { > 0.04 } with our final LGBM model:
V:\projects\ppuodz-ML.4.1\shared\graph.py:1262: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead. corr = round(corr.applymap(pd.to_numeric), 2)
The TARGET variable (loans with payment difficulties) is most correlated with credit ratings obtained from external sources. The correlation is very weak but still significant.
Because the datatypes of features vary we had to use different methods to measure the strength and significance of each pair:
- Chi-Squared Test: Assesses independence between two categorical variables. For bool-bool pairs due to categorical nature.
- Point Biserial Correlation: Measures correlation between a binary and a continuous variable. For bool-numerical pairs to account for mixed data types.
- Spearman's Rank Correlation: Assesses monotonic relationship between two continuous variables. Used for numerical-numerical pairs (for non-normally distributed data).
Since the Chi-Squared test outputs an unbound statistic/value which can't be directly compared to pointbiserialr or Spearman Rank we have converted them to a ` Cramér's V:` value which is normalized between 0 and 1. This was done to make the values in the matrix more uniform however we must note that Cramér's V and Spearman's correlation coefficients are fundamentally different statistics and generally can't be directly compared.
Corelation With the Target Variable¶
Our target variable TARGET show whether the given application had any late payments (value = 1), we can see that no single feature is strongly correlated with it:
Out[10]:
| Coefficient | P-Value | |
|---|---|---|
| Column | ||
| ExtSource3 | -0.151 | 0.000 |
| ExtSource1 | -0.131 | 0.000 |
| ExtSource2 | -0.125 | 0.000 |
| OccupationType | 0.073 | 0.000 |
| PrevRatioRejectedAccepted_cats | 0.071 | 0.000 |
| PrevRatioRejectedAccepted_cats_2 | 0.071 | 0.000 |
| DaysEmployed | 0.070 | 0.000 |
| OrganizationType | 0.065 | 0.000 |
| MaxbureaudaysCredit | 0.064 | 0.000 |
| NameEducationType | 0.059 | 0.000 |
| PrevCreditReceivedRequestedDiff | 0.056 | 0.000 |
| PrevAmtDownPaymentSum | -0.052 | 0.000 |
| OwnCarAge | 0.049 | 0.000 |
| DaysBirth | 0.049 | 0.000 |
| MeanbureauamtCreditSumDebt | 0.045 | 0.000 |
| MaxbureaudaysCreditEnddate | 0.041 | 0.000 |
| PrevAvgYieldGroup | 0.040 | 0.000 |
| CodeGender | 0.040 | 0.000 |
| FlagDocument3 | 0.038 | 0.000 |
| AmtGoodsPrice | -0.031 | 0.000 |
| DaysIdPublish | 0.030 | 0.000 |
| NameFamilyStatus | 0.028 | 0.000 |
| MaxbureaudaysEnddateFact | 0.022 | 0.000 |
| AmtCredit | -0.020 | 0.001 |
| MeanbureauamtCreditSum | -0.017 | 0.005 |
| AmtAnnuity | 0.000 | 0.979 |
The chart below shows the relationship between selected categorical variables and loan status. E.g. a significantly higher proportion of loans taken out by males had issues.
Out[14]:
CategoricalDtype(categories=['< 25% Rejected', '> 25% Rejected', 'All Accepted', 'No Previous App.'], ordered=False, categories_dtype=object)
Relationships Between Numerical and Categorical Variables¶
The charts below show pairs of numerical and categorical features (including some binned numerical features) that have a signficant relationships and at least a small effect size (eta_squared>0.01) based on the non-parametric Kruskal-Wallis Test (one-way ANOVA on ranks) testing whether samples originate from the same distribution.
*It's similar to the Mann–Whitney U test but allows comparing more than 2 groups
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
V:\projects\ppuodz-ML.4.1\shared\graph.py:1441: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped = _df.groupby(c)[y_target] V:\projects\ppuodz-ML.4.1\shared\graph.py:1454: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. group_counts = _df.groupby(c).size()
Analyzing Credit Scores (ExtSource1)¶
ExtSource1/2/3 are the variables most strongly correlated with the target variable, they indicate client credit scores obtained from external sources. While th correlation coeficients are very low (only slightly above 0.1) we'll look a bit more into these scores because ussually credit ratings tend be the most useful metric when estimating the risk of specific loans:
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2649400095.py:17: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
colors = plt.cm.get_cmap("tab10", 4)
Summary for combined model:
Logit Regression Results
==============================================================================
Dep. Variable: TARGET No. Observations: 109589
Model: Logit Df Residuals: 109585
Method: MLE Df Model: 3
Date: Wed, 01 May 2024 Pseudo R-squ.: 0.1047
Time: 12:44:24 Log-Likelihood: -25636.
converged: True LL-Null: -28634.
Covariance Type: nonrobust LLR p-value: 0.000
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
const 0.6002 0.040 14.829 0.000 0.521 0.680
ExtSource1 -2.0989 0.061 -34.382 0.000 -2.219 -1.979
ExtSource2 -1.9640 0.060 -32.654 0.000 -2.082 -1.846
ExtSource3 -2.7793 0.062 -44.483 0.000 -2.902 -2.657
==============================================================================
This is a simple Logistic model that just uses the credit scores to estimate the target variable. The confidence interval shows the the standard deviation of the residuals from a combined logistic regression model (residuals in this context are the differences between the observed values (y_combined) and the predicted probabilities).
Gennerally the explained variabity (Pseudo R-squared) is very quite low at only 0.1047 however the model itself is statistically significant (LLR p-value = 0.0)
Out[18]:
| Coefficient | Standard Error | P-Value | Conf. Interval Lower | Conf. Interval Upper | |
|---|---|---|---|---|---|
| const | 0.600 | 0.040 | 0.0 | 0.521 | 0.680 |
| ExtSource1 | -2.099 | 0.061 | 0.0 | -2.219 | -1.979 |
| ExtSource2 | -1.964 | 0.060 | 0.0 | -2.082 | -1.846 |
| ExtSource3 | -2.779 | 0.062 | 0.0 | -2.902 | -2.657 |
Normalized credit ratings from three sources are inversely related to default risk, with ExtSource3 having the strongest influence. We can see that a basic Logistic model can already provide a reasonably high result (AUC = 0.74). However, we have to note that the results are based on the full training set and are only provided for EDA/feature analysis purposes. Full statistical modelling will be done in further sections.
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:6: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:11: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:6: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:11: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:6: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\3414605899.py:11: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
We can see that while the the external credit are clearly related to default risk their explanatory power is somewhat limited because there is still a large amount of overlap (especially for ExtSource2, however it's coeefficient in our logistical model is similar to that of ExtSource1.
Previous Application History¶
Did any clients had previously applied for loans with Home Credit and what were the outcomes of their applications?
Out[21]:
PrevRatioRejectedAccepted_cats All Accepted 190370 > 25% Rejected 66215 < 25% Rejected 34079 No Previous App. 16847 Name: count, dtype: int64
Did any applicants default on any previous loans?
Out[22]:
TotalDefaults_cats No Defaults 304114 1 Defaulted Loans 3397 Name: count, dtype: int64
Suprisingly we can see that a ~1% of all applicants who were granted a loans have previously had payment difficulties with a previous loans at Home Credit. This is quite interesting considering that gennerally credit instituions are reluctant to offer loans again to problematic clients.
Total "Defaults"/Loans With Payment Difficulties per applicant:
Out[24]:
| TotalDefaults | count | proportion | |
|---|---|---|---|
| 0 | 0.0 | 304114 | 98.895% |
| 1 | 1.0 | 3177 | 1.033% |
| 2 | 2.0 | 163 | 0.053% |
| 3 | 3.0 | 38 | 0.012% |
| 4 | 4.0 | 11 | 0.004% |
| 5 | 5.0 | 4 | 0.001% |
| 6 | 6.0 | 3 | 0.001% |
| 7 | 7.0 | 1 | 0.000% |
Previous Loan History and Default Risk¶
The chart below shows the default rate based on whether applicant has previous applied for loans with Home Cred:
No Previous App. - no previous applications for client found (i.e. new clients)
All Accepted - all previous applications were accepted
< 25% Rejected - less than 1/4 applications were rejected
> 25% Rejected - more than 1/4 applications were rejected
Out[25]:
PrevRatioRejectedAccepted_cats All Accepted 190370 > 25% Rejected 66215 < 25% Rejected 34079 No Previous App. 16847 Name: count, dtype: int64
Interestingly we can see that while applicants whose previous loans were rejected are significantly more likely to default when finally given a loan previous clients with no failed applications have a higher default risk than new clients.
This likely limits the usefulness of the previous_application table because only a small proportion of clients have previously rejected applications
<Figure size 1000x600 with 0 Axes>
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2318662498.py:5: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2318662498.py:10: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:10: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:15: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
<Figure size 1200x600 with 0 Axes>
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:10: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:15: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:10: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot( C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2778785946.py:15: FutureWarning: `shade` is now deprecated in favor of `fill`; setting `fill=True`. This will become an error in seaborn v0.14.0; please update your code. sns.kdeplot(
We can clearly see that clients who had run into payment issues with their past loans tend to have a signficantly lower credit ExtSource3 however there is almost no difference with other scores. This incidates that the data from Home Credit itself is only included in the third rating (which might explain its higher explantatory power in our Logistic model)
Loan Purposes¶
V:\projects\ppuodz-ML.4.1\shared\graph.py:1511: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. source_df.groupby(group_col)[target_col]
C:\Users\Paulius\AppData\Local\Temp\ipykernel_18696\2838244680.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
features_matrix.groupby("NameContractType")["TARGET"]
<Figure size 1000x600 with 0 Axes>
